Super resolution techniques allow delivery of high-quality images and video when the input sources are low resolution relative to the display. Ideally, super resolution techniques should recover details and maintain sharpness of edges on their own. In reality, artifacts crop up in the super resolution process. For example, some edges will have artificial sharpness, like the transition between two colors in oil paintings without the corresponding increase in overall detail. These artifacts may occur in upscaled images at higher resolutions than the original image. These images will be referred to as high resolution images, as no super resolution process is applied to obtain them.
Typically, some post-processing methods such as image enhancements will assist in alleviating these artifacts by enhancing what details remain. One example of post-processing image enhancement includes peaking, but even peaking cannot always add the details necessary to make the image look natural.
The process of dithering reduces quantization error and prevents large-scale patterns such as color banding image by adding noise to image before quantization. Dithering can use fixed patterns or random noise. Typically, it involves adding high frequency noise, often referred to blue noise. The term ‘blue’ generally refers to the power spectrum, wherein blue noise has very few or no low frequency components. Because it has very few or no low frequency components, the dither noise can substitute for the missing details and reduce or conceal artifacts including the oil painting effect discuss above.